Field Crops Research最新文献

{"title":"Time-series NDVI and greenness spectral indices in mid-to-late growth stages enhance maize yield estimation","authors":"Dayun Feng ,&nbsp;Hongye Yang ,&nbsp;Kexin Gao ,&nbsp;Xiuliang Jin ,&nbsp;Zhenhai Li ,&nbsp;Chenwei Nie ,&nbsp;Guoqiang Zhang ,&nbsp;Liang Fang ,&nbsp;Linli Zhou ,&nbsp;Huirong Guo ,&nbsp;Zhijie Jia ,&nbsp;Bo Ming ,&nbsp;Keru Wang ,&nbsp;Shaokun Li","doi":"10.1016/j.fcr.2025.110069","DOIUrl":"10.1016/j.fcr.2025.110069","url":null,"abstract":"<div><div>Accurate and timely satellite-based yield estimation is crucial for agricultural policy and food security. The Normalized Difference Vegetation Index (NDVI), which represents canopy greenness, is widely used to predict crop yields. However, most studies have developed empirical models based only on the relationship between instantaneous spectral information and final yield, ignoring the canopy physiological changes that can be captured by spectral monitoring during crop yield formation, which limits the applicability of these models. In this study, based on time-series NDVI data, we found that the trend of NDVI changes in the mid-to-late stages of maize is closely related to yield, and accordingly constructed four greenness spectral indices: NDVI decline rate (DR), average daily accumulation of NDVI (ADA), time-series NDVI standard deviation (STD), and leaves greenness duration (LGD). We assessed the validity of the GSIs under two scenarios: (1) When the time-series data were consistent across years, using three strategies, namely utilizing the complete NDVI datasets, estimating yield in advance, and accounting for missing data due to meteorological conditions; (2) We ask whether the GSIs remain valid when the time-series data are inconsistent across years. Results under time-series consistency showed that combining these GSIs derived from the complete NDVI dataset with the third-period NDVI achieved the highest model accuracy (<em>R</em>^<em>2</em> = 0.7, rRMSE = 12.3 %). Approximately one month before harvest, GSIs improved estimation accuracy (<em>R</em>^<em>2</em> = 0.661, rRMSE = 13.2 %), increasing <em>R</em>^<em>2</em> by 0.023 and reducing rRMSE by 0.4 %. When NDVI data were incomplete due to meteorological conditions, GSIs still enhanced yield estimation, increasing <em>R</em>^<em>2</em> by 0.007–0.077 and reducing rRMSE by 0.1 %-1.1 %. Even with the inconsistency of time-series data across years, the accuracy of yield estimation improved by 28 % after integrating GSIs. These results demonstrate the adaptability and reliability of GSIs under different conditions.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110069"},"PeriodicalIF":5.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-task learning model driven by climate and remote sensing data collaboration for mid-season cotton yield prediction","authors":"Huihan Wang ,&nbsp;Yuanshuai Dai ,&nbsp;Qiushuang Yao ,&nbsp;Lulu Ma ,&nbsp;Ze Zhang ,&nbsp;Xin Lv","doi":"10.1016/j.fcr.2025.110070","DOIUrl":"10.1016/j.fcr.2025.110070","url":null,"abstract":"<div><div>Accurate prediction of cotton yield is critical for agricultural policy, production management, and food security. We aimed to enhance regional-scale cotton yield estimation by clarifying the respective contributions of climate and remote sensing variables and identifying optimal time windows for early prediction. We focused on the 8th Division of the Xinjiang Production and Construction Corps in China, using field survey data, Sentinel-2A imagery, and meteorological records from 2021 and 2023. Key variables were selected using Sequential Forward Selection and Structural Equation Modeling. Partial Least Squares Regression (PLSR), Random Forest, and XGBoost models were developed to estimate cotton yields and assess the performance of different data combinations and time periods. Additionally, a multi-task learning (MTL) framework was proposed to support dynamic early-season yield prediction, with 15-day interval time windows. Results showed that climate factors indirectly influenced yield by affecting vegetation status, while remote sensing data contributed significantly to prediction accuracy, particularly during key growth stages. Climate data alone generally outperformed remote sensing data, although their combination consistently improved model accuracy and stability. PLSR achieved the best performance at the T6 window (flowering and boll-setting stage) with R² = 0.60 and RMSE = 605.7 kg/ha. The MTL model demonstrated increasing accuracy as the season progressed, achieving optimal performance 60 days before harvest (R² = 0.71, RMSE = 519.7 kg/ha). We provide a cost-effective, timely, and simple framework for predicting cotton yields at a regional scale using publicly available data. The findings support improved agricultural production management and contribute to food security initiatives.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110070"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sugar kelp application for sustainable potato production in Prince Edward Island: Impacts on soil, greenhouse gas emissions, and yield","authors":"Arishma Khan ,&nbsp;Raheleh Malekian ,&nbsp;Travis J. Esau ,&nbsp;Gurpreet S. Selopal ,&nbsp;Kuljeet S. Grewal","doi":"10.1016/j.fcr.2025.110068","DOIUrl":"10.1016/j.fcr.2025.110068","url":null,"abstract":"<div><h3>Context</h3><div>Sugar kelp (SK) is a promising organic fertilizer with the potential to enhance crop yield, improve soil health, and reduce environmental impacts. However, its specific effects on soil quality, crop productivity, and particularly its role in climate change mitigation are still not well understood.</div></div><div><h3>Objectives</h3><div>This study evaluated the effects of SK, as seaweed-based organic fertilizer, and its combinations with IF on soil health, emissions of CO₂ and N₂O, as well as CH₄ uptake, potato growth and yield during the 2023 and 2024 growing seasons in Prince Edward Island, Canada’s largest potato-producing province.</div></div><div><h3>Methods</h3><div>Field experiments were conducted over a two-year period (2023 and 2024). In 2023, treatments included: SK alone (2 tons ha^-¹), IF alone (meeting the full nitrogen (N) requirement), SK + IF (50 %-50 % N), and control (no fertilizer). In 2024, treatments were: IF alone, SK + IF (full N), SK + IF (80 % N), and control. The study measured soil organic matter, pH, P₂O₅, K₂O, Ca, Mg, Cu, Zn, S, Mn, Fe, Na, Al, and NO₃^-, along with the Normalized Difference Vegetation Index (NDVI), and potato yield. Soil emission of CO₂ and N₂O emissions, and soil CH₄ uptake were also measured using the Li-COR trace gas analyzer.</div></div><div><h3>Results</h3><div>Soil pH, organic matter, calcium, magnesium, and cation exchange capacity remained stable across treatments. Trace elements such as copper, iron, and zinc also showed minimal variation. However, the SK application significantly increased soil sodium concentrations in both years (p &lt; 0.05). In 2024, nitrate (NO₃⁻-N) levels were significantly higher in the IF treatments than in the control. Cumulative CO₂ emissions and CH₄ uptake did not differ significantly among treatments in either year. IF-only treatments showed the highest cumulative N₂O emissions, whereas treatments combining SK with reduced IF significantly lowered cumulative N₂O emissions to levels similar to the control. These reduced-emission treatments maintained NDVI values and potato yields comparable to those of the full IF treatments, both of which outperformed the control.</div></div><div><h3>Conclusions</h3><div>These results suggest that combining SK with reduced IF can sustain potato yields while significantly lowering N₂O emissions. These findings highlight the potential of SK in sustainable fertilizer strategies; however, further long-term research and economic analysis are necessary to evaluate its broader viability in agriculture.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110068"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Straw mulching optimized the root and canopy structure of soybean by reducing the topsoil temperature before blooming period","authors":"Zongsheng Wu ,&nbsp;Yupeng Zhu ,&nbsp;Qirui Li ,&nbsp;Ruidong Li ,&nbsp;Simon Willcock ,&nbsp;Viktória Vona ,&nbsp;Robert Dunn ,&nbsp;András Vér ,&nbsp;Yifan Xu ,&nbsp;Jianxin Hua ,&nbsp;Cailong Xu ,&nbsp;Wenwen Song ,&nbsp;Cunxiang Wu","doi":"10.1016/j.fcr.2025.110067","DOIUrl":"10.1016/j.fcr.2025.110067","url":null,"abstract":"<div><h3>Context</h3><div>The soybean seed yield in the Huang-Huai-Hai (HHH) region is challenged by high temperatures before blooming. Straw mulching can act to reduce topsoil temperature. However, little is known about whether changes in topsoil temperature contribute to the optimization of soybean root and canopy structure and, ultimately, yield.</div></div><div><h3>Objective</h3><div>The aim of this study is to investigate the effects of straw mulching on soybean topsoil temperature, root growth, and canopy structure in the HHH region, China.</div></div><div><h3>Methods</h3><div>A randomized block design was adopted (2020–2023) in the field, including three straw treatments: straw removing (SR), straw mulching (SM), and straw crushing (SC). Topsoil temperature, root morphology, leaf area index (LAI), light transmittance, canopy photosynthesis, dry matter accumulation, and seed yield of soybean under different treatments were measured. Furthermore, the test results were validated by pot experiment (LT: topsoil cooling, CT: topsoil non-cooling) in 2024.</div></div><div><h3>Results</h3><div>Before soybean blooming, the highest topsoil temperature was 28.47℃ in SR, followed by 27.47℃ in SC and 26.95℃ in SM. Compared to SR and SC, the root length, root surface area, root volume and root dry weight of SM increased by an average of 26.04 %, 27.79 %, 29.13 % and 38.82 %, respectively. Soybean root dry matter weight was significantly positively correlated (P &lt; 0.01) with the LAI and above-ground dry matter accumulation. Compared to SR and SC, Fv/Fm, Y(II), and ETR under SM treatment increased by 8.38 %, 7.94 %, and 7.73 %, respectively. Y(II) of the LT treatment was also significantly (P &lt; 0.05) increased by 17.53 % compared to CT. Among the three treatments, soybean canopy photosynthetic rate and seed yield under SM treatment were, on average, significantly increased by 9.97 %, and 11.87 %, respectively. Furthermore, we identified the LAI characteristics of high-yield soybean canopy: 2.22 &lt;LAI&lt; 2.44 in the upper layer, 1.71 &lt;LAI&lt; 3.21 in the middle layer, and LAI&gt; 0.62 in the lower layer.</div></div><div><h3>Conclusion and implications</h3><div>These findings imply that regulating topsoil temperature through straw mulching optimizes root and canopy development, improving soybean yield. This study provides insights into mitigating heat stress and enhancing sustainable soybean production in warm climates.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110067"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of combined high temperature and water stress on soybean growth and physiological processes in a temperature gradient chamber","authors":"Yanchao Liu ,&nbsp;Keisuke Mizuta ,&nbsp;Masahiro Morokuma ,&nbsp;Masanori Toyota","doi":"10.1016/j.fcr.2025.110063","DOIUrl":"10.1016/j.fcr.2025.110063","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context or problem&lt;/h3&gt;&lt;div&gt;Under global warming and growing water scarcity, soybean frequently faces concurrent high temperature stress and water stress, which together impair biomass accumulation and physiological performance. Although the separate effects of high temperature stress or water stress are well documented, their combined impact across realistic temperature gradients remains poorly defined, making it difficult to predict crop performance under simultaneous stresses.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective or research question&lt;/h3&gt;&lt;div&gt;This study assessed how combined high temperature stress and water stress affect soybean above-ground dry matter accumulation, growth dynamics, physiological processes, and water use efficiency to elucidate the mechanisms combined stress responses.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;A two-year experiment (2023–2024) was conducted in a temperature gradient chamber divided into four thermal zones—low, medium-low, medium-high, high—and two irrigation regimes: normal irrigation and water stress. Above-ground dry matter was determined at harvest, and periodic measurements of leaf area and dry weight were used to derive growth rate and net assimilation. Key physiological processes were monitored throughout development. Relationships among physiological and growth parameters were analyzed using regression and multivariate methods. Water use efficiency was defined as dry matter produced per unit of irrigation water.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Water stress reduced above-ground dry matter by 39–40 % compared with normal irrigation, and the high-temperature zone produced 35–36 % less dry matter than the low-temperature zone. Combined high temperature stress and water stress caused over a 60 % decline in dry matter relative to the low-temperature zone under normal irrigation. Before beginning pod stage, net assimilation rate and crop growth rate were strongly correlated, indicating that assimilation drives early biomass accumulation. Under simultaneous high temperature and water stress, reductions in leaf water potential led to lower stomatal conductance and photosynthetic rate, which in turn suppressed net assimilation, growth, and water use efficiency. Although water stress alone increased water use efficiency in 2023, elevated temperature stress in 2024 reversed this effect under water stress, demonstrating that high temperature stress exacerbates water stress’s negative impact on water use efficiency.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Simultaneous high temperature stress and water stress severely limit soybean biomass by disrupting leaf water potential, stomatal regulation, and photosynthetic performance. The water stress-induced improvement in water use efficiency is negated under elevated temperatures, amplifying biomass loss.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Implications or significance&lt;/h3&gt;&lt;div&gt;These findings clarify the physiological interactions under combined high temperature and water stress, info","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110063"},"PeriodicalIF":5.6,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of phenological uniformity on prediction accuracy of wheat yield and biomass","authors":"Yandong Yang ,&nbsp;Qing Li ,&nbsp;Qinwen Lin ,&nbsp;Huimin Wang ,&nbsp;Yi Shi ,&nbsp;Gengchen Wu ,&nbsp;Yue Mu ,&nbsp;Dong Jiang ,&nbsp;Seishi Ninomiya","doi":"10.1016/j.fcr.2025.110066","DOIUrl":"10.1016/j.fcr.2025.110066","url":null,"abstract":"<div><h3>Context</h3><div>Accurate prediction of wheat yield and biomass is essential for breeding new cultivars and optimizing field management. The accuracy of yield and biomass predictions can be affected by the phenological phase of data collection. However, phenological transitions are gradual, and wheat fields rarely consist of a single phenological stage. The influence of phenological uniformity (PU) on prediction accuracy has been largely overlooked, particularly in multi-cultivar study areas.</div></div><div><h3>Objective</h3><div>This study aimed to quantitatively evaluate PU, to classify wheat phenological stages using hyperspectral data collected by an unmanned aerial vehicle (UAV), to identify the optimal growth stage for yield and biomass prediction, and to assess the impact of PU on prediction accuracy.</div></div><div><h3>Methods</h3><div>A two-year field experiment was conducted using 210 wheat cultivars with diverse phenological stages, and time-series hyperspectral images were collected using an UAV. The study first defined and quantitatively evaluated PU. Subsequently, the classification accuracies of five models were compared to identify the most effective approach for phenological stage classification. Hyperspectral data collected at four key growth stages were then used to determine the optimal stage for yield and biomass prediction. Finally, datasets with varying PU were constructed to predict yield and biomass, and the influence of PU on prediction accuracy was assessed.</div></div><div><h3>Results</h3><div>PU of wheat exhibited a fluctuating trend throughout the growth stages, with most values ranging between 0.5 and 0.8. Hyperspectral data enabled effective discrimination of key phenological stages, among which the end-to-end mixhop superpixel-based graph convolutional networks (EMS-GCN) model achieved the highest classification accuracy, with an overall accuracy of 86.2 %. The PLSR model achieved the most accurate predictions of both yield (R² = 0.692, RMSE = 1.091 t/ha, CV = 0.152) and biomass (R² = 0.827, RMSE = 1.873 t/ha, CV = 0.113) at the flowering stage. The results of yield and biomass prediction based on datasets with varying PU values indicated a positive correlation between PU and prediction accuracy.</div></div><div><h3>Conclusions</h3><div>Accurate classification of key wheat phenological stages can be achieved by combining deep learning with hyperspectral data. The flowering stage is the optimal period for yield and biomass prediction. PU positively correlates with the prediction accuracy of yield and biomass.</div></div><div><h3>Implications</h3><div>This study emphasizes the important role of PU in wheat yield and biomass prediction, and accurate monitoring of PU can provide theoretical guidance for data collection. This is of great significance to the development of precision agriculture and guiding field management.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110066"},"PeriodicalIF":5.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in lysine content in rice caryopses during grain filling and in response to mid-season nitrogen management","authors":"Kuanyu Zhu ,&nbsp;Wenli Tao ,&nbsp;Yi Jiang ,&nbsp;Weiyang Zhang ,&nbsp;Zhiqin Wang ,&nbsp;Junfei Gu ,&nbsp;Jianhua Zhang ,&nbsp;Jianchang Yang","doi":"10.1016/j.fcr.2025.110062","DOIUrl":"10.1016/j.fcr.2025.110062","url":null,"abstract":"<div><h3>Context and problem</h3><div>Lysine (Lys) is the first limiting essential amino acid in rice, and its biosynthesis in caryopses is regulated by nitrogen (N) management. However, the temporal dynamics of Lys content in earlier-flowering superior caryopses (SCs) and later-flowering inferior caryopses (ICs), as well as its distribution across caryopsis layers during grain filling, remains poorly understood. Moreover, variations in Lys anabolism between SCs and ICs, among rice varieties, and in response to mid-season N application have not been fully elucidated.</div></div><div><h3>Objective</h3><div>The study aimed to (1) characterize changes in Lys content in both SCs and ICs during grain filling across different rice varieties; (2) examine the relationship between Lys content in different layers of a caryopsis during grain filling and final Lys content in brown and milled rice; and (3) identify N management practices that enhance Lys biosynthesis in rice grains.</div></div><div><h3>Methods</h3><div>Two field experiments were conducted for three years. The first experiment involved six rice varieties grown under conventional N management. The second tested four mid-season N treatments, i.e., no N application (N0), N application at panicle initiation (N1), pistil and stamen differentiation (N2), and heading initiation (N3), using two representative rice varieties.</div></div><div><h3>Results</h3><div>Compared to SCs, ICs exhibited a lower peak increase rate (PIR) of Lys content during grain filling, with varietal differences observed. Varieties with higher PIR had higher Lys accumulation at maturity, especially in ICs. Lys content in the mid and inner layers of developing grains was closely associated with the proportion of Lys in milled rice relative to brown rice at maturity. Enhanced activities of enzymes involved in Lys biosynthesis, rather than catabolism, contributed to higher PIR and Lys retention. Mid-season N application (N1 - N3) significantly increased PIR, Lys biosynthesis enzyme activities, and Lys and total amino acid contents in both brown and milled rice, while also increasing grain yield. Notably, N1 and N2 increased the proportion of Lys in milled rice and enhanced taste quality, whereas N3 reduced both.</div></div><div><h3>Conclusions</h3><div>Boosting the PIR of Lys and its accumulation in the middle and inner caryopsis layers during grain filling is key to increasing Lys content in milled rice. N application during panicle differentiation effectively promotes this strategy while enhancing both grain yield and eating quality.</div></div><div><h3>Implications</h3><div>This study offers a practical approach to improving the nutrient quality of rice through optimized mid-season N management focused on enhancing Lys biosynthesis in developing grains.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110062"},"PeriodicalIF":5.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of maize, soybean, and mung bean intercropping systems in East Java, Indonesia","authors":"Naoko Kawasaki ,&nbsp;Tjeerd Jan Stomph ,&nbsp;Lotte Suzanne Woittiez ,&nbsp;Wahyu Muji Laksono ,&nbsp;Denis Muba Pandapotan Simanihuruk ,&nbsp;Devi Fitriani ,&nbsp;Narendra Duhita ,&nbsp;Wopke van der Werf","doi":"10.1016/j.fcr.2025.110015","DOIUrl":"10.1016/j.fcr.2025.110015","url":null,"abstract":"<div><h3>Context</h3><div>Maize (<em>Zea mays</em> L.)-soybean (<em>Glycine max</em> L. (Merr.)) intercropping can enhance land and resource use efficiency, but its performance depends on species interactions, environmental conditions, and management. There is little information on intercrop performance under different configurations without major resource deficits in a tropical climate.</div></div><div><h3>Objectives</h3><div>We determined the land use efficency of simultaneously sown, flood irrigated maize-soybean intercropping systems comprising maize, soybean and mung bean in the tropical climate of East Java with moderate fertilizer input, and evaluated how narrow-wide row systems and incorporation of mung bean changed the land use efficiency and productivity.</div></div><div><h3>Methods</h3><div>Experiments were done during four growing seasons. Two replacement systems used two maize and four soybean rows in alternating strips at local standard spacing or at reduced maize inter-row distances to improve soybean radiation interception (narrow-wide row system). Two additive systems introduced mung bean (<em>Vigna radiata</em> L.) or additional soybean rows.</div></div><div><h3>Results</h3><div>Additive intercrops had higher land use efficiency (average LER ≈ 1.10) than sole crops or replacement systems (LER = 0.93). The narrow-wide row system improved soybean performance and overall LER without significantly affecting maize yield. Soybean outperformed mung bean in additive systems, while mung bean struggled near maize rows.</div></div><div><h3>Synthesis and implications</h3><div>Simultaneously sown maize-soybean replacement intercropping systems with recommended fertilizer inputs and sufficient water did not have a land use advantage in East Java. Additive systems provided marginal benefits. These findings align with emerging evidence that yield benefits of intercropping are small under simultaneous sowing and adequate resource inputs.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110015"},"PeriodicalIF":5.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the differences in the effects of management practices on maize yield and water use efficiency in the North China Plain and Northwest China: A meta-analysis","authors":"Boyue Zhang ,&nbsp;Ziyi Yang ,&nbsp;Ruopu Wang ,&nbsp;Shaozhong Kang ,&nbsp;Taisheng Du ,&nbsp;Ling Tong ,&nbsp;Jian Kang ,&nbsp;Jia Gao ,&nbsp;Risheng Ding","doi":"10.1016/j.fcr.2025.110065","DOIUrl":"10.1016/j.fcr.2025.110065","url":null,"abstract":"<div><h3>Context</h3><div>The North China Plain (NCP) and Northwest (NW) China are both crucial maize-producing regions; but their management practices, such as water management, nitrogen management, mulching and straw return, differ significantly. In the NCP, maize production primarily relies on seasonal precipitation, while the NW region depends heavily on irrigation. Quantifying the differences in the effects of these management practices on maize yield and water use efficiency (WUE) between the NCP and NW is challenging due to variations in geographic location, climate, soil properties, and human factors.</div></div><div><h3>Objective</h3><div>The objectives of this study were to quantify the differences in the impacts of different management practices on maize yield and WUE between the NCP and NW, and assess the relative importance of these management practices on maize yield and WUE in each regions.</div></div><div><h3>Method</h3><div>This study conducted a comprehensive meta-analysis of 1058 datasets from the NCP and NW, providing insights into the differences in the effects of management practices on maize yield and WUE. In addition, random forest analysis was employed to quantify the importance of different management practices on maize yield and WUE in the two regions.</div></div><div><h3>Results and conclusions</h3><div>The management practices had a greater effect on maize yield and WUE in the NW than that in the NCP, attributed to the NW's lower precipitation, soil organic carbon and total nitrogen contents. Among these practices, nitrogen management had the most significant impact on yield and WUE, followed by mulching practices, straw return, and water management. Specifically, drip irrigation significantly increased maize WUE by 31.0 % compared with flood irrigation. The most pronounced improvements in yield (71.1 %) and WUE (64.6 %) were observed at a nitrogen application rate of 150–225 kg ha⁻¹. Additionally, plastic film mulching demonstrated more significant yield and WUE compared to straw mulching. Mild deficit irrigation improved WUE without significantly affecting yield, while severe deficit irrigation increased WUE but significantly reduced yield. Nitrogen management and mulching practices improved maize yield and WUE in the NW more effectively compared with the NCP. In the relative importance analysis, considering environmental factors (meteorological and soil factors), nitrogen application and irrigation amounts were essential for affecting maize yield and WUE in the NW, whereas irrigation amount and seasonal precipitation were more critical in the NCP.</div></div><div><h3>Significance</h3><div>This study provides scientific support for improving maize yield and WUE in both the NCP and NW through specific management practices.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110065"},"PeriodicalIF":5.6,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjusted CBA-Wheat model for predicting aboveground biomass in winter wheat from hyperspectral data","authors":"Jingshu Chen ,&nbsp;Jiaye Yu ,&nbsp;Yang Meng ,&nbsp;Limin Gu ,&nbsp;Francesco Rossi ,&nbsp;Xiaokang Zhang ,&nbsp;Wenchao Zhen ,&nbsp;Zhenhai Li","doi":"10.1016/j.fcr.2025.110060","DOIUrl":"10.1016/j.fcr.2025.110060","url":null,"abstract":"<div><h3>Context or problem</h3><div>Crop aboveground biomass (AGB) is a key indicator of photosynthesis and carbon cycle dynamics in agricultural ecosystems. The availability of accurate, real-time AGB data enables efficient resource management and precision farming. The crop biomass algorithm for wheat (CBA-Wheat) estimates winter wheat AGB using vegetation index (VI) and Zadoks stage (ZS), but acquiring ZS data through field surveys is challenging for large-scale applications.</div></div><div><h3>Objective or research question</h3><div>This study aimed to optimize the CBA-Wheat model by incorporating the concept of the relative day of the year (RDOY) as a replacement for ZS and combining it with VI to enhance the performance of the wheat growth model.</div></div><div><h3>Methods</h3><div>We proposed the concept of RDOY to replace the traditional ZS, thereby optimizing the CBA-Wheat model. The study used data from Xiaotangshan, Beijing, from 2013 to 2020 for model development. The validation dataset included 2021 Xiaotangshan data, 2010 suburban Beijing data, and 2012 Yucheng, Shandong data for testing the model’s temporal and spatial transferability. Additionally, we compared the performance of the CBA-Wheat_RDOY model with machine learning models, including Partial Least Squares Regression (PLSR) and Random Forest (RF).</div></div><div><h3>Results</h3><div>We found that the modified CBA-Wheat_RDOY model, utilizing the modified simple ratio vegetation index (MSR) as an input parameter, achieved the highest AGB estimation accuracy, with a coefficient of determination (R²) of 0.82 and a root mean square error (RMSE) of 1.71 t/ha. This result surpassed the performance of partial least squares regression (R² = 0.78, RMSE = 1.48 t/ha) and random forest (R² = 0.73, RMSE = 2.03 t/ha) models when RDOY was introduced.</div></div><div><h3>Conclusions</h3><div>Our findings highlight the effectiveness of introducing RDOY in improving the accuracy of winter wheat biomass estimation within the CBA-Wheat model. Moreover, RDOY is a superior alternative to traditional phenological observations and can potentially enhance the performance of conventional machine learning models.</div></div><div><h3>Implications or significance</h3><div>Compared with existing algorithms, the CBA-Wheat_RDOY model, grounded in RDOY, not only responds sensitively to various phenological stages but also exhibits improved inversion accuracy. This approach holds promising potential for enhancing the timeliness and spatial extrapolation of winter wheat AGB predictions, advancing precision agriculture and ecosystem management.</div></div>","PeriodicalId":12143,"journal":{"name":"Field Crops Research","volume":"333 ","pages":"Article 110060"},"PeriodicalIF":5.6,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}